Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Notch Signaling Reprograms Glial Lipid Metabolism to Promote Hypoxia Resistance.

bioRxiv : the preprint server for biology·2026
Same author

Multiscale metabolic mapping of lung tissue via coregistered mass spectrometry and nonlinear optical imaging.

Science advances·2026
Same author

Illuminating aging with multimodal optical metabolic imaging.

Science advances·2026
Same author

AMPK/ SIRT1 signaling pathway activation acts on PGC-1α/ PPARγ to alleviate sepsis-acquired weakness.

Cell death discovery·2026
Same author

3D multi-omics tumour atlases: from technology to biology and clinical translation.

Nature reviews. Cancer·2026
Same author

Hepatocyte hedgehog signaling controls ferroptosis to alleviate aging-related organ dysfunction.

JCI insight·2026

Related Experiment Video

Updated: Jul 16, 2025

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:08

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

73

Early cancer detection by SERS spectroscopy and machine learning.

Lingyan Shi1, Yajuan Li2, Zhi Li2

  • 1Shu Chien-Gene Lay Department of Bioengineering, UC San Diego, La Jolla, CA, 92093, USA. l2shi@ucsd.edu.

Light, Science & Applications
|September 15, 2023
PubMed
Summary

This study introduces a novel method for early cancer detection by combining surface-enhanced Raman spectroscopy (SERS) of serum molecular fingerprints with machine learning algorithms.

More Related Videos

Surface-enhanced Resonance Raman Scattering Nanoprobe Ratiometry for Detecting Microscopic Ovarian Cancer via Folate Receptor Targeting
07:54

Surface-enhanced Resonance Raman Scattering Nanoprobe Ratiometry for Detecting Microscopic Ovarian Cancer via Folate Receptor Targeting

Published on: March 25, 2019

8.2K
Author Spotlight: Advancing SERS Technology: Au@Carbon Dot Nanoprobes for Label-Free Analysis and Imaging
06:19

Author Spotlight: Advancing SERS Technology: Au@Carbon Dot Nanoprobes for Label-Free Analysis and Imaging

Published on: June 9, 2023

1.6K

Related Experiment Videos

Last Updated: Jul 16, 2025

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:08

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

73
Surface-enhanced Resonance Raman Scattering Nanoprobe Ratiometry for Detecting Microscopic Ovarian Cancer via Folate Receptor Targeting
07:54

Surface-enhanced Resonance Raman Scattering Nanoprobe Ratiometry for Detecting Microscopic Ovarian Cancer via Folate Receptor Targeting

Published on: March 25, 2019

8.2K
Author Spotlight: Advancing SERS Technology: Au@Carbon Dot Nanoprobes for Label-Free Analysis and Imaging
06:19

Author Spotlight: Advancing SERS Technology: Au@Carbon Dot Nanoprobes for Label-Free Analysis and Imaging

Published on: June 9, 2023

1.6K

Area of Science:

  • Biomedical Engineering
  • Analytical Chemistry
  • Computational Biology

Background:

  • Early cancer detection significantly improves patient outcomes.
  • Current diagnostic methods face limitations in sensitivity and specificity for detecting multiple cancer types simultaneously.

Purpose of the Study:

  • To develop and validate a novel, integrated approach for the early detection of multiple cancers.
  • To leverage the power of SERS spectroscopy and machine learning for enhanced diagnostic accuracy.

Main Methods:

  • Utilized surface-enhanced Raman spectroscopy (SERS) to analyze serum molecular fingerprints.
  • Applied advanced machine learning algorithms to classify spectral data for cancer identification.
  • Integrated SERS data with machine learning models for a comprehensive diagnostic strategy.

Main Results:

  • Demonstrated the potential of SERS to capture unique molecular signatures in serum associated with different cancers.
  • Achieved high accuracy in distinguishing between cancerous and non-cancerous samples using the integrated approach.
  • Showcased the feasibility of detecting multiple cancer types through this combined methodology.

Conclusions:

  • The integration of SERS spectroscopy and machine learning offers a promising new avenue for early, multi-cancer detection.
  • This approach has the potential to revolutionize cancer diagnostics by providing a sensitive and specific screening tool.
  • Further validation in larger clinical studies is warranted to translate this technology into routine practice.